How to Process Data? 6 Stages for Success

The amount of data that is generated each second is astounding – each person on the internet creates 1.7 MB of data per second. But, it’s more astounding that many businesses collect massive amounts of data that go unused because it doesn’t get processed. By following data processing steps, businesses can unlock massive potential by gaining deep insights for informed decision-making. Let’s take a look at how to process data effectively and efficiently.

Coming Up

1. What is Data Processing?

2. What are the Stages of Data Processing?

3. What is the Future of Data Processing?

4. What are the Types of Data Processing?

5. What are Data Processing Methods?

6. What are Examples of Data Processing?

7. Derive Value from Your Data!

What is Data Processing?

Data processing is the transformation of data from its raw form into usable information. Think of it like cooking. If you have raw ingredients, you have the building blocks to make a great meal. But, you have to go through the process to transform the ingredients from their original state to make something that will be easily digestible.

Like cooking, data processing is a multistep process. In some cases, it involves data scientists and data engineers. That is, unless you make use of an automation solution that can process data for you so that your human resources can continue working on other tasks. Automation solutions have helped to democratise data processing, much like air fryers have helped to level up every amateur chefs’ game.

Data processing allows businesses to take advantage of the data that they collect so they can devise better business plans and strategies. It also provides for the ability to have a competitive edge. With data processing, data can be curated into readable formats like dashboards, charts, and graphs so that it can be used to make decisions and better understand situations.

What are the Stages of Data Processing?

Although data can enter a business from so many various sources and in a variety of formats, the steps for how to process data tends to look the same for everyone.

The stages of data processing include:

1. Data Collection

You can’t process what you don’t have. The data processing cycle begins with data collection, in which raw data is pulled from different sources. It should be defined and accurate in order to be used. Raw data may include profit/loss statements, website cookies, monetary figures, text, photos, and more.

2. Data Preparation

In order to be able to use the data, it must be cleansed, sorted, and filtered. This is vital to remove any inaccurate, incomplete, or redundant data. The results and insights gleaned will only be as good as the input, which in this case, is the raw data.

You can utilise automation software to cleanse data for you so that nothing gets missed in this crucial step. This increases the level of accuracy, thereby reducing errors, which saves both time and money.

3. Data Inputting

Next, data is converted into a machine readable format so it can be fed into the processing unit. For example, data entry may come in the form of a scanner, keyboard, or automation tool.

4. Data Processing

With the aid of machine learning and artificial intelligence, raw data undergoes processing to generate the output in a format that is understandable and readable. The processes applied will depend on the input method and source of the data being processed.

5. Data Output

To take it back to our cooking analogy, the data output is the finished meal! In the context of data, this could be a graph, table, audio, video, documents, etc. It is data represented in the intended format for use.

6. Data Storage

Data and metadata then get stored for future use. At a later date, you can apply advanced analytics or other applications, leverage the historical data for reference, or quick retrieval.

What is the Future of Data Processing?

The future of data processing has already arrived. It’s all about data processing in the cloud!

Cloud technology makes data processing more efficient and cost effective. With big data moving to the cloud, companies get to take advantage of all that data has to offer. All data is connected and exists in a centralised, accessible, and secure location.

Cloud technology is able to integrate seamlessly with existing and legacy systems, remain up-to-date, and is increasingly secure because security updates are automatic.

Cloud-based automation solutions are making data processing a possibility for organisations of all sizes. For example, SolveXia is a low-code/no-code solution that offers out-of-the-box data processing capabilities so that an organisation, even one without a dedicated IT team, can make the most out of their data.

What are the Types of Data Processing?

While the steps for data processing typically follow the same flow, there are a few different approaches for how to process data.

These include:

1. Batch Processing

Batch processing means that data is collected and processed in groups (or batches). This method is best-suited for large amounts of data. A real-world example of batch processing is often found when it comes to payroll systems.

2. Online Processing

Data is fed into the CPU automatically as soon as it becomes available. This is necessary in cases where the data must be processed for use immediately. For example, this is the case with barcode scanning at a store.

3. Real-time Processing

As the raw data is input, the data gets processed in real-time processing. For this type of data processing, think of ATMs. As soon as you enter how much money you want from the machine, the system processes your input and spits out the cash.

4. Multiprocessing

This is also called parallel processing. It refers to when a single computer system breaks down data into frames and processes it using two or more CPUs.

5. Time-Sharing

This type of data processing occurs when a computer allocates resources and data in time slots to multiple users at the same time.

What are Data Processing Methods?

Along with the different types of data processing, there are also three main methods of data processing.

1. Manual Data Processing

As the name implies, this is when data is processed by hand, or manually. A human is responsible for every step of the data processing cycle, from the first step of data collection all the way through to filtering, calculating, and producing outputs. While it is thought to be a relatively low-cost method that doesn’t require many tools, it is time-consuming and has immense opportunity cost. The costs can also add up because of labour and the loss of time.

2. Mechanical Data Processing

Mechanical data processing relies on devices and machines to process data. These devices could be calculators or typewriters, for example. This method is feasible for simple data processing tasks and will result in less errors than the manual option. However, it’s not really scalable as the volume of data increases daily.

3. Electronic Data Processing

Modern times call for modern solutions- hence, electronic data processing. Automation tools fall into this realm as data is processed using software and programs. The software receives a set of instructions and then transforms data into the desired output.

While it may come off as the most expensive method, it can actually result in cost savings over time because of its efficiency and reduction of errors. The high reliability, accuracy, and scalability of automation solutions make them a desirable solution for any business who wishes to process data.

What are Examples of Data Processing?

Once you have an example of data processing, it’s hard to unsee data processing everywhere you go. The truth is that data processing, just like data, is happening all around you.

For example, you can witness data processing in action when you turn to:

• A stock market trading graph
• Online shopping when you see recommended products
• A Tesla that warns you of a pedestrian or other car that is in your path
• Geo-targeted ads that you receive on social media

From a business standpoint, you have to process data to create financial statements, execute payroll, understand customer behaviour, review marketing campaigns, and much more. With the help of an automation solution, you can have all your data and use it, too!

Automation solutions will collect, cleanse, and transform data in the way you need it so that you can continue to focus on your responsibilities that require human thought.

Rather than having your team spend countless hours and days searching, cleaning, and calculating data, you can let the software do the hard work for you. This way, everyone gets to reap the benefits of having usable data.

Derive Value from Your Data!

Regardless of the business you’re running, data is entering your walls and you need to know how to process data if you want to be able to utilise it for all that it is worth.

Automated data processing will save you time, money, and reduce errors. In turn, you get to derive all the value from having the insights you need to make informed decisions.

Want to see how implementing a data processing solution will improve your organisation’s productivity? Request a demo to see the magic in action of raw data transforming into the insights and information you seek.